Business Intelligence Data Scientist – Sr. Associate - JPMorgan Private Bank

Join the Business Intelligence team within the JPMorgan Private Bank and help shape strategy with advanced analytics and AI.  
You will work on high-impact initiatives that improve sales productivity, business development, and decision-making through data-driven insights.  
You will partner closely with business, sales, marketing, and technology teams to turn complex data into practical solutions.  
If you enjoy combining rigorous modeling with real-world business outcomes, this role offers the opportunity to grow your impact and help modernize how insights are delivered. 

As a Business Intelligence Data Scientist within the JPMorgan Private Bank Business Intelligence team, you will lead analytical initiatives that shape business strategy through data-driven insights.  
You will design, build, and deploy predictive models and analytics solutions using internal and external data to create actionable recommendations. J 
You will collaborate with leaders across business, sales, and marketing to embed analytics into day-to-day decision-making and continuous improvement.  
You will help evolve reporting into proactive, personalized insights by prototyping and applying modern AI approaches, including machine learning and large language models.

Job responsibilities 

• Partner with business, sales, marketing, and technology teams to define requirements and deliver analytics solutions that drive measurable outcomes. 
• Design, develop, and deploy machine learning and advanced analytics solutions for complex business problems. 
• Apply statistical analysis, predictive modeling, and AI techniques to generate insights from large, complex datasets. 
• Perform exploratory data analysis to identify trends, patterns, and opportunities for growth and productivity improvements. J
• Communicate insights and recommendations through clear narratives, visualizations, and presentations tailored to stakeholders. 
• Prototype AI-enabled approaches, including large language models and automation, to deliver personalized, context-aware insights and recommendations. 
• Identify, evaluate, and onboard internal and external datasets to support analytics and modeling initiatives. 
• Assess data quality and reliability, and implement automated validation and monitoring to maintain data integrity. 
• Collaborate with engineering partners to implement scalable data pipelines, model deployment workflows, and analytics infrastructure. 
• Ensure governance, security, documentation, and lineage standards are met across data and model integration processes. 
• Translate business needs into clear technical specifications and contribute production-quality code across the analytics lifecycle.

Required qualifications, capabilities, and skills 

• Bachelor’s degree in data science, computer science, statistics, mathematics, or a related technical field. 
• 3 years of experience in data science, machine learning, or advanced analytics roles. 
• Advanced proficiency in Python for data analysis, modeling, and production-grade implementation. 
• Advanced proficiency in SQL for data extraction, transformation, and analysis. 
• Demonstrated ability to build, evaluate, and deploy predictive models and analytics solutions end-to-end. 
• Strong statistical and analytical problem-solving skills with the ability to translate complex results into actionable recommendations. 
• Experience designing, deploying, and operating production machine learning pipelines and services. 
• Working knowledge of AI implementation in software development contexts, including modernization of legacy codebases. 
• Ability to partner effectively across technical and non-technical teams to drive delivery and adoption. 

Preferred qualifications, capabilities, and skills 

• Experience supporting sales, marketing, or productivity analytics use cases in a financial services environment. 
• Experience integrating external datasets and managing ongoing relationships with data providers or vendors. 
• Familiarity with large language model applications, evaluation approaches, and responsible AI considerations. 
• Experience with scalable data engineering patterns for analytics, including orchestration and automated monitoring. 
• Strong storytelling skills, including the ability to influence stakeholders using clear visuals and executive-ready narratives.